100 research outputs found
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to build machine learning
model that can continually learn new concepts from a few data samples, without
forgetting knowledge of old classes.
The challenges of FSCIL lies in the limited data of new classes, which not
only lead to significant overfitting issues but also exacerbates the notorious
catastrophic forgetting problems. As proved in early studies, building sample
relationships is beneficial for learning from few-shot samples. In this paper,
we promote the idea to the incremental scenario, and propose a Sample-to-Class
(S2C) graph learning method for FSCIL.
Specifically, we propose a Sample-level Graph Network (SGN) that focuses on
analyzing sample relationships within a single session. This network helps
aggregate similar samples, ultimately leading to the extraction of more refined
class-level features.
Then, we present a Class-level Graph Network (CGN) that establishes
connections across class-level features of both new and old classes. This
network plays a crucial role in linking the knowledge between different
sessions and helps improve overall learning in the FSCIL scenario. Moreover, we
design a multi-stage strategy for training S2C model, which mitigates the
training challenges posed by limited data in the incremental process.
The multi-stage training strategy is designed to build S2C graph from base to
few-shot stages, and improve the capacity via an extra pseudo-incremental
stage. Experiments on three popular benchmark datasets show that our method
clearly outperforms the baselines and sets new state-of-the-art results in
FSCIL
Late onset post-LASIK keratectasia with reversal and stabilization after use of latanoprost and corneal collagen cross-linking
We report a case of late onset keratectasia after laser in situ keratomileusis (LASIK) and its quick reversal and stabilization after use of latanoprost and riboflavin/ultraviolet-A corneal collagen cross-linking (CXL). A 39-year-old man with normal intraocular pressure developed a rapid deterioration of vision in his left eye 6 years after LASIK-retreatment for high myopic astigmatism. Keratectasia was diagnosed by corneal topography and ultrasound pachymetry. After two months of treatment with latanoprost and a minor intraocular pressure reduction, uncorrected distance visual acuity improved from 20/100 to 20/20 and corneal topography showed reversal of keratectasia. CXL was performed after the reversal to achieve long-term stabilization. At 1, 3, 6, 13 and 39 months followup exams after the CXL, stable vision, refraction, and topography were registered. This case shows that keratectasia may rapidly occur several years after LASIK and that a quick reversal and stabilization may be achieved by use of latanoprost followed by CXL
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision
Vehicle-to-everything (V2X) perception is an innovative technology that
enhances vehicle perception accuracy, thereby elevating the security and
reliability of autonomous systems. However, existing V2X perception methods
focus on static scenes from mainly vehicle-based vision, which is constrained
by sensor capabilities and communication loads. To adapt V2X perception models
to dynamic scenes, we propose to build V2X perception from road-to-vehicle
vision and present Adaptive Road-to-Vehicle Perception (AR2VP) method. In
AR2VP,we leverage roadside units to offer stable, wide-range sensing
capabilities and serve as communication hubs. AR2VP is devised to tackle both
intra-scene and inter-scene changes. For the former, we construct a dynamic
perception representing module, which efficiently integrates vehicle
perceptions, enabling vehicles to capture a more comprehensive range of dynamic
factors within the scene.Moreover, we introduce a road-to-vehicle perception
compensating module, aimed at preserving the maximized roadside unit perception
information in the presence of intra-scene changes.For inter-scene changes, we
implement an experience replay mechanism leveraging the roadside unit's storage
capacity to retain a subset of historical scene data, maintaining model
robustness in response to inter-scene shifts. We conduct perception experiment
on 3D object detection and segmentation, and the results show that AR2VP excels
in both performance-bandwidth trade-offs and adaptability within dynamic
environments
Multi-Label Continual Learning using Augmented Graph Convolutional Network
Multi-Label Continual Learning (MLCL) builds a class-incremental framework in
a sequential multi-label image recognition data stream. The critical challenges
of MLCL are the construction of label relationships on past-missing and
future-missing partial labels of training data and the catastrophic forgetting
on old classes, resulting in poor generalization. To solve the problems, the
study proposes an Augmented Graph Convolutional Network (AGCN++) that can
construct the cross-task label relationships in MLCL and sustain catastrophic
forgetting. First, we build an Augmented Correlation Matrix (ACM) across all
seen classes, where the intra-task relationships derive from the hard label
statistics. In contrast, the inter-task relationships leverage hard and soft
labels from data and a constructed expert network. Then, we propose a novel
partial label encoder (PLE) for MLCL, which can extract dynamic class
representation for each partial label image as graph nodes and help generate
soft labels to create a more convincing ACM and suppress forgetting. Last, to
suppress the forgetting of label dependencies across old tasks, we propose a
relationship-preserving constrainter to construct label relationships. The
inter-class topology can be augmented automatically, which also yields
effective class representations. The proposed method is evaluated using two
multi-label image benchmarks. The experimental results show that the proposed
way is effective for MLCL image recognition and can build convincing
correlations across tasks even if the labels of previous tasks are missing
EVNet: An Explainable Deep Network for Dimension Reduction
Dimension reduction (DR) is commonly utilized to capture the intrinsic
structure and transform high-dimensional data into low-dimensional space while
retaining meaningful properties of the original data. It is used in various
applications, such as image recognition, single-cell sequencing analysis, and
biomarker discovery. However, contemporary parametric-free and parametric DR
techniques suffer from several significant shortcomings, such as the inability
to preserve global and local features and the pool generalization performance.
On the other hand, regarding explainability, it is crucial to comprehend the
embedding process, especially the contribution of each part to the embedding
process, while understanding how each feature affects the embedding results
that identify critical components and help diagnose the embedding process. To
address these problems, we have developed a deep neural network method called
EVNet, which provides not only excellent performance in structural
maintainability but also explainability to the DR therein. EVNet starts with
data augmentation and a manifold-based loss function to improve embedding
performance. The explanation is based on saliency maps and aims to examine the
trained EVNet parameters and contributions of components during the embedding
process. The proposed techniques are integrated with a visual interface to help
the user to adjust EVNet to achieve better DR performance and explainability.
The interactive visual interface makes it easier to illustrate the data
features, compare different DR techniques, and investigate DR. An in-depth
experimental comparison shows that EVNet consistently outperforms the
state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC
Muon Flux Measurement at China Jinping Underground Laboratory
China Jinping Underground Laboratory (CJPL) is ideal for studying solar-,
geo-, and supernova neutrinos. A precise measurement of the cosmic-ray
background would play an essential role in proceeding with the R\&D research
for these MeV-scale neutrino experiments. Using a 1-ton prototype detector for
the Jinping Neutrino Experiment (JNE), we detected 264 high-energy muon events
from a 645.2-day dataset at the first phase of CJPL (CJPL-I), reconstructed
their directions, and measured the cosmic-ray muon flux to be
cms. The observed angular distributions indicate the leakage of
cosmic-ray muon background and agree with the simulation accounting for Jinping
mountain's terrain. A survey of muon fluxes at different laboratory locations
situated under mountains and below mine shaft indicated that the former is
generally a factor of larger than the latter with the same vertical
overburden. This study provides a convenient back-of-the-envelope estimation
for muon flux of an underground experiment
Performance of the 1-ton Prototype Neutrino Detector at CJPL-I
China Jinping Underground Laboratory (CJPL) provides an ideal site for solar,
geo-, and supernova neutrino studies. With a prototype neutrino detector
running since 2017, containing 1-ton liquid scintillator (LS), we tested its
experimental hardware, performed the physics calibration, and measured its
radioactive backgrounds, as an early stage of the Jinping Neutrino Experiment
(JNE). We investigated the radon background and implemented the nitrogen
sealing technology to control it. This paper presents the details of these
studies and will serve as a key reference for the construction and optimization
of the future large detector at JNE
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