190 research outputs found
Graph Reasoning Transformer for Image Parsing
Capturing the long-range dependencies has empirically proven to be effective
on a wide range of computer vision tasks. The progressive advances on this
topic have been made through the employment of the transformer framework with
the help of the multi-head attention mechanism. However, the attention-based
image patch interaction potentially suffers from problems of redundant
interactions of intra-class patches and unoriented interactions of inter-class
patches. In this paper, we propose a novel Graph Reasoning Transformer (GReaT)
for image parsing to enable image patches to interact following a relation
reasoning pattern. Specifically, the linearly embedded image patches are first
projected into the graph space, where each node represents the implicit visual
center for a cluster of image patches and each edge reflects the relation
weight between two adjacent nodes. After that, global relation reasoning is
performed on this graph accordingly. Finally, all nodes including the relation
information are mapped back into the original space for subsequent processes.
Compared to the conventional transformer, GReaT has higher interaction
efficiency and a more purposeful interaction pattern. Experiments are carried
out on the challenging Cityscapes and ADE20K datasets. Results show that GReaT
achieves consistent performance gains with slight computational overheads on
the state-of-the-art transformer baselines.Comment: Accepted in ACM MM202
The Role of 7,8-Dihydroxyflavone in Preventing Dendrite Degeneration in Cortex After Moderate Traumatic Brain Injury
Our previous research showed that traumatic brain injury (TBI) induced by controlled cortical impact (CCI) not only causes massive cell death, but also results in extensive dendrite degeneration in those spared neurons in the cortex. Cell death and dendrite degeneration in the cortex may contribute to persistent cognitive, sensory, and motor dysfunction. There is still no approach available to prevent cells from death and dendrites from degeneration following TBI. When we treated the animals with a small molecule, 7,8-dihydroxyflavone (DHF) that mimics the function of brain-derived neurotrophic factor (BDNF) through provoking TrkB activation reduced dendrite swellings in the cortex. DHF treatment also prevented dendritic spine loss after TBI. Functional analysis showed that DHF improved rotarod performance on the third day after surgery. These results suggest that although DHF treatment did not significantly reduced neuron death, it prevented dendrites from degenerating and protected dendritic spines against TBI insult. Consequently, DHF can partially improve the behavior outcomes after TBI
Centralized Feature Pyramid for Object Detection
Visual feature pyramid has shown its superiority in both effectiveness and
efficiency in a wide range of applications. However, the existing methods
exorbitantly concentrate on the inter-layer feature interactions but ignore the
intra-layer feature regulations, which are empirically proved beneficial.
Although some methods try to learn a compact intra-layer feature representation
with the help of the attention mechanism or the vision transformer, they ignore
the neglected corner regions that are important for dense prediction tasks. To
address this problem, in this paper, we propose a Centralized Feature Pyramid
(CFP) for object detection, which is based on a globally explicit centralized
feature regulation. Specifically, we first propose a spatial explicit visual
center scheme, where a lightweight MLP is used to capture the globally
long-range dependencies and a parallel learnable visual center mechanism is
used to capture the local corner regions of the input images. Based on this, we
then propose a globally centralized regulation for the commonly-used feature
pyramid in a top-down fashion, where the explicit visual center information
obtained from the deepest intra-layer feature is used to regulate frontal
shallow features. Compared to the existing feature pyramids, CFP not only has
the ability to capture the global long-range dependencies, but also efficiently
obtain an all-round yet discriminative feature representation. Experimental
results on the challenging MS-COCO validate that our proposed CFP can achieve
the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX
object detection baselines.Comment: Code: https://github.com/QY1994-0919/CFPNe
Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation
Thanks to the advantages of the friendly annotations and the satisfactory
performance, Weakly-Supervised Semantic Segmentation (WSSS) approaches have
been extensively studied. Recently, the single-stage WSSS was awakened to
alleviate problems of the expensive computational costs and the complicated
training procedures in multi-stage WSSS. However, results of such an immature
model suffer from problems of \emph{background incompleteness} and \emph{object
incompleteness}. We empirically find that they are caused by the insufficiency
of the global object context and the lack of the local regional contents,
respectively. Under these observations, we propose a single-stage WSSS model
with only the image-level class label supervisions, termed as
\textbf{W}eakly-\textbf{S}upervised \textbf{F}eature \textbf{C}oupling
\textbf{N}etwork (\textbf{WS-FCN}), which can capture the multi-scale context
formed from the adjacent feature grids, and encode the fine-grained spatial
information from the low-level features into the high-level ones. Specifically,
a flexible context aggregation module is proposed to capture the global object
context in different granular spaces. Besides, a semantically consistent
feature fusion module is proposed in a bottom-up parameter-learnable fashion to
aggregate the fine-grained local contents. Based on these two modules,
\textbf{WS-FCN} lies in a self-supervised end-to-end training fashion.
Extensive experimental results on the challenging PASCAL VOC 2012 and MS COCO
2014 demonstrate the effectiveness and efficiency of \textbf{WS-FCN}, which can
achieve state-of-the-art results by and mIoU on PASCAL VOC
2012 \emph{val} set and \emph{test} set, mIoU on MS COCO 2014
\emph{val} set, respectively. The code and weight have been released
at:~\href{https://github.com/ChunyanWang1/ws-fcn}{WS-FCN}.Comment: accepted by TNNL
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Occlusion perturbation presents a significant challenge in person
re-identification (re-ID), and existing methods that rely on external visual
cues require additional computational resources and only consider the issue of
missing information caused by occlusion. In this paper, we propose a simple yet
effective framework, termed Erasing, Transforming, and Noising Defense Network
(ETNDNet), which treats occlusion as a noise disturbance and solves occluded
person re-ID from the perspective of adversarial defense. In the proposed
ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature
map to create an adversarial representation with incomplete information,
enabling adversarial learning of identity loss to protect the re-ID system from
the disturbance of missing information. Secondly, we introduce random
transformations to simulate the position misalignment caused by occlusion,
training the extractor and classifier adversarially to learn robust
representations immune to misaligned information. Thirdly, we perturb the
feature map with random values to address noisy information introduced by
obstacles and non-target pedestrians, and employ adversarial gaming in the
re-ID system to enhance its resistance to occlusion noise. Without bells and
whistles, ETNDNet has three key highlights: (i) it does not require any
external modules with parameters, (ii) it effectively handles various issues
caused by occlusion from obstacles and non-target pedestrians, and (iii) it
designs the first GAN-based adversarial defense paradigm for occluded person
re-ID. Extensive experiments on five public datasets fully demonstrate the
effectiveness, superiority, and practicality of the proposed ETNDNet. The code
will be released at \url{https://github.com/nengdong96/ETNDNet}
CellBRF: A Feature Selection Method for Single-Cell Clustering Using Cell Balance and Random Forest
Motivation
Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results
We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. Availability and implementation
All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF
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