191 research outputs found
Analyzing Cellular Apoptosis Through Monitoring Mitochondrial Membrane Potential Changes with JC-10
Group Sampling for Unsupervised Person Re-identification
Unsupervised person re-identification (re-ID) remains a challenging task,
where the classifier and feature representation could be easily misled by the
noisy pseudo labels towards deteriorated over-fitting. In this paper, we
propose a simple yet effective approach, termed Group Sampling, to alleviate
the negative impact of noisy pseudo labels within unsupervised person re-ID
models. The idea behind Group Sampling is that it can gather a group of samples
from the same class in the same mini-batch, such that the model is trained upon
group normalized samples while alleviating the effect of a single sample. Group
sampling updates the pipeline of pseudo label generation by guaranteeing the
samples to be better divided into the correct classes. Group Sampling
regularizes classifier training and representation learning, leading to the
statistical stability of feature representation in a progressive fashion.
Qualitative and quantitative experiments on Market-1501, DukeMTMC-reID, and
MSMT17 show that Grouping Sampling improves the state-of-the-arts by up to
2.2%~6.1%. Code is available at https://github.com/wavinflaghxm/GroupSampling
P2RBox: A Single Point is All You Need for Oriented Object Detection
Oriented object detection, a specialized subfield in computer vision, finds
applications across diverse scenarios, excelling particularly when dealing with
objects of arbitrary orientations. Conversely, point annotation, which treats
objects as single points, offers a cost-effective alternative to rotated and
horizontal bounding boxes but sacrifices performance due to the loss of size
and orientation information. In this study, we introduce the P2RBox network,
which leverages point annotations and a mask generator to create mask
proposals, followed by filtration through our Inspector Module and Constrainer
Module. This process selects high-quality masks, which are subsequently
converted into rotated box annotations for training a fully supervised
detector. Specifically, we've thoughtfully crafted an Inspector Module rooted
in multi-instance learning principles to evaluate the semantic score of masks.
We've also proposed a more robust mask quality assessment in conjunction with
the Constrainer Module. Furthermore, we've introduced a Symmetry Axis
Estimation (SAE) Module inspired by the spectral theorem for symmetric matrices
to transform the top-performing mask proposal into rotated bounding boxes.
P2RBox performs well with three fully supervised rotated object detectors:
RetinaNet, Rotated FCOS, and Oriented R-CNN. By combining with Oriented R-CNN,
P2RBox achieves 62.26% on DOTA-v1.0 test dataset. As far as we know, this is
the first attempt at training an oriented object detector with point
supervision
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Object detection via inaccurate bounding boxes supervision has boosted a
broad interest due to the expensive high-quality annotation data or the
occasional inevitability of low annotation quality (\eg tiny objects). The
previous works usually utilize multiple instance learning (MIL), which highly
depends on category information, to select and refine a low-quality box. Those
methods suffer from object drift, group prediction and part domination problems
without exploring spatial information. In this paper, we heuristically propose
a \textbf{Spatial Self-Distillation based Object Detector (SSD-Det)} to mine
spatial information to refine the inaccurate box in a self-distillation
fashion. SSD-Det utilizes a Spatial Position Self-Distillation \textbf{(SPSD)}
module to exploit spatial information and an interactive structure to combine
spatial information and category information, thus constructing a high-quality
proposal bag. To further improve the selection procedure, a Spatial Identity
Self-Distillation \textbf{(SISD)} module is introduced in SSD-Det to obtain
spatial confidence to help select the best proposals. Experiments on MS-COCO
and VOC datasets with noisy box annotation verify our method's effectiveness
and achieve state-of-the-art performance. The code is available at
https://github.com/ucas-vg/PointTinyBenchmark/tree/SSD-Det.Comment: accepted by ICCV 202
Streptococcus suis Sequence Type 7 Outbreak, Sichuan, China
An outbreak of Streptococcus suis serotype 2 emerged in the summer of 2005 in Sichuan Province, and sporadic infections occurred in 4 additional provinces of China. In total, 99 S. suis strains were isolated and analyzed in this study: 88 isolates from human patients and 11 from diseased pigs. We defined 98 of 99 isolates as pulse type I by using pulsed-field gel electrophoresis analysis of SmaI-digested chromosomal DNA. Furthermore, multilocus sequence typing classified 97 of 98 members of the pulse type I in the same sequence type (ST), ST-7. Isolates of ST-7 were more toxic to peripheral blood mononuclear cells than ST-1 strains. S. suis ST-7, the causative agent, was a single-locus variant of ST-1 with increased virulence. These findings strongly suggest that ST-7 is an emerging, highly virulent S. suis clone that caused the largest S. suis outbreak ever described
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